The expected value of a token sequence ab representing a PER when a is red and b is blue is: The expected value of the cases when the token pair ab is not a PER name is the sum of expected values of four cases: Table 2), which after simplification, is given by: The ratio between the cases when ab is a PER versus when ab is not a PER is: where the rationality values of tokens ab, a and b of being a PER, red ball or blue ball respectively.

the probabilities of a as a surname (red ball) and b as a first-name (blue ball) are:  The form of Equation (4) is similar to the concept of odds likelihood O(h), first introduced in Duda et al.


One way to achieve this is to employ a decision tree (Sekine 98) to select the best possible candidates.

it is difficult to use the decision tree to handle multiple relationships between conflicting NEs, and to perform incremental updates of rationality values in situations where the number, distribution and relationships in possible NEs are uncertain.


For spoken dialogue systems, this aspect of style can be important to optimize the analysis of a user's input.

social bonding is performed by adapting to a common interaction style (Brown and Levinson, 1987;Okada et al., 1999).


Therefore, people can choose the relevant clusters of documents on the map to get relevant documents.

it is impossible for one map to encompass the continuously growing data source.


Comparing the three algorithms we see that overall, kNN and the category-based method exhibit comparable performance (with the exception of measuring similarity by L1 distance, when the category-based method outperforms kNN by a margin of about 5 points; statistical significance p<0.001).

their performance is different in different frequency ranges: for lower frequencies kNN is more accurate (e.g., for L1 distance, p<0.001).


The level of -phrases can fairly easily be derived from syntax.


Slow speech rate is imitated by decreasing, and fast rate by increasing this single parameter.

the results are not quite satisfactory yet, because some of the steps of the overall procedure for assigning phrase breaks were manually corrected.


In an analysis of the APA newswire corpus (a corpus of over 28 million words), we found that almost half (47%) of the word types were compounds.

the compounds accounted for a small portion of the overall token count (7%).


Intuitively, knowing what the modifier is should help us in guessing the head of a compound.

constructing a plausible head-prediction term based on modifier-head dependencies is not straightforward.


Thus, these preliminary experiments indicate that an approach to integrating compound and simple word predictions along the lines sketched at the beginning of this section, and in particular the version of the model in which modifier predictions are penalized, is feasible.

the model is clearly in need of further refinement, given that the improvement over the baseline model is currently minimal.


It is important to capture effective patterns and features from the training sense tagged data in WSD.

if there is noise in the training sense tagged data, it makes difficult to disambiguate word senses effectively.


In the case of the telephone translation system, the information of the language used is self-evident; at least, the speaker knows; so there are little needs and advantages of developing a multilingual speech identification system.

speech data in video do not always have the information about the language used.


It can be analysed that contextual rules are constructed using registered words in a P-DIC and estimating pronunciation module makes some errors.

'not registered' words also show relatively good performance.


The huge work for constructing the rules, however, cannot help depending on a lot of hands, and it is also difficult to modify the rules.

a technique for tagging dialogue acts has been proposed so far (Araki et al., 2001).


Our model was built using the British National Corpus (100 million words).

our model was built using only a part-of-speech tagged corpus.


In a critical part of his derivation Simon gives an initial probability to a new word found in the corpus as it introduces some new meaning not expressed by previous words.

as the number of words increases new ideas are frequently expressed not in single words, but in multi-word phrases or compound words.


This result appears to vindicate Simon's derivation.

whether Simon's derivation is entirely valid or not, the results in Figure 12 are a new confirmation of Zipf's original law in an extended form.


This figure shows that Dist(D("do")) is smaller than Dist(D("electronic")), which reflects our linguistic intuition that words co-occurring with "electronic" are more biased than those with "do".

dist(d("cipher")) is smaller than dist(d("do")), which contradicts our linguistic intuition.


In algebraic terms, concatenation left-distributes over union but does not right-distribute over union in general.

our solution is to provide a pair of concatenation operators: , which gives priority to the left, and ≺, which gives priority to the right: Rule (1) marks the second Y in YYY, but rule (2) marks the first Y.


Just as Collins uses rules to identify heads and arguments and thereby lexicalize trees, Chiang uses nearly the same rules to reconstruct derivations: each training example is broken into elementary trees, with each head child remaining attached to its parent, each argument broken into a substitution node and an initial root, and each adjunct broken off as a modifier auxiliary tree.

in this experiment we view the derived trees in the Treebank as incomplete data, and try to reconstruct the derivations (the complete data) using the Inside-Outside algorithm.


That is to say, a document which has actually been authored conveys more meaning than just stating "I am a valid document relative to the specification".

in a closed-world environment as we have been discussing until now, that additional meaning has no explicit counterpart in the knowledge-base; it is only represented implicitly in the abstract content tree, in a form which is not perspicuous and would be difficult to re-use for the authoring of other documents or to share with other processes.


(1999), the variable Ú represented not only the lexical verb but also its syntactic relation to the noun: either direct object, subject of an intransitive, or subject of a transitive verb.

the relationship between the underlying, semantic arguments of a verb and the syntac-tic roles in a sentence is not always straightforward.


This is in fact the case, with child, band, and team appearing among the top ten nouns for both positions.

play also exhibits an alternation between the direct object and subject of intransitive positions for music, role, and game.


This example illustrate the complex interaction between verb sense and alternation behavior: "The band played" and the "The music played" are considered to belong to different senses of play by WordNet (Fellbaum, 1998) and other word sense inventories.

it is interesting to note that nouns from both the broad senses of play, "play a game" and "play music", participate in both alternations.


Similarly, the direct object and subject of intransitive slots of increase are assigned to the same cluster.

in an example of how the model can cluster semantically related verbs that do not share the same alternation behavior, the direct object slot of reduce and the subject of transitive slot of exceed are groups together with increase.


We also made a 3x2   Table 6 are under 5%, it is confirmed that there exists associations among expression patterns and impressions for each readership.

the strength of the associations is low as for the respondents with readership "engineer" or "researcher", who have expertise, because of lower values of Cramer's V, while the strength of the association is high as for those who has readership "unconcerned" or "commoner" because of higher values of V. Because all graphs slope up from left to right in Figures 1, 2 and 3, the expression pattern 2.0 (expressing what the purpose of the technology is) is the most effective to make a title impressive (more precisely, regarded as "comprehensible", "evoked positive feelings" and "interesting") for readers with most of readership, especially the "unconcerned" readers or "commoner".


As a result, titles using expression pattern 2.0 (expressing what the purpose of the technology is) in obligatory component is the most effective in getting the general reader's comprehension, positive feelings, and interest.

the more expertise in a technical field the reader has, the less the reader tends to be influenced.


As the technique for stochastic parsing of spoken language, Den has suggested a new idea for detecting and parsing self-repaired expressions, however, the phenomena with which the framework can cope are restricted (Den, 1995).

our method provides the most plausible dependency structures for natural speeches by utilizing stochastic information.


About 86.5% of inversions appear at the last bunsetsu.

73 dependencies, providing 5.4% of 1,362 turns consisting of more than two units, are over two utterance units.


how frequently the word sequence T is used as a title for a document.

of course, the probability for the word sequence T to be used as a title for any document is definitely influenced by the correctness of the word order in T. the factor whether the words in the sequence T are common words or not will also have great influence on the chance of seeing the sequence T as a title.


In the old framework for title generation, term P(T) is used for ordering title words into a correct sequence.

term P(T) is not only influenced by the word order in T, but also whether words in T are common words.


Their work describes the modularization of an NLG system and the tasks of each module.

they do not indicate what kind of tools can be used by each module.


The maximum entropy system performed competitively with the best systems on the English verbs in SENSEVAL-1 and SENSEVAL-2 (Dang and Palmer, 2002).

while SENSEVAL-2 made it possible to compare many different approaches over many different languages, data for the Chinese lexical sample task was not made available in time for any systems to compete.


Broad-coverage lexical resources such as WordNet are extremely useful in applications such as Word Sense Disambiguation (Leacock, Chodorow, Miller 1998) and Question-Answering (Pasca and Harabagiu 2001).

they often include many rare senses while missing domain-specific senses.


An alternative considers the number of pairs of elements whose similarity exceeds a certain threshold (Guha, Rastogi, Kyuseok 1998).

this may cause undesirable mergers when there are a large number of pairs whose similarities barely exceed the threshold.


An example of the first approach considers the average entropy of the clusters, which measures the purity of the clusters (Steinbach, Karypis, and Kumar 2000).

maximum purity is trivially achieved when each element forms its own cluster.


The word dog belongs to both the Person and Animal classes.

in the newspaper corpus, the Person sense of dog is at best extremely rare.


("New Year reception") is the subject in 11a while it is the object in 11b.


Consequently, many researchers have focused on producing accurate word segmenters for Chinese text indexing (Teahan et al, 2001;Brent and Tao, 2001).

we have recently observed that low accuracy word segmenters often yield superior retrieval performance (Peng et al, 2002).


However, as segmentation accuracy improves, the tokens behave more like true words and the retrieval engine begins to behave more conventionally.

after a point, when the second regime is reached, retrieval performance no longer increases with improved segmentation accuracy, and eventually begins to decrease.


This method is commonly applied on mobile phones.

there are two problems with this method.


Finding a most probable class assignment to the entities and relations is equivalent to finding the assignment of all the variables in the belief network that maximizes the joint probability.

this mostprobable-explanation (MPE) inference problem is intractable (Roth, 1996) Figure 3: Belief network of 3 entity nodes and 6 relation nodes (undirected cycles), which is exactly the case in our network.


In a feature-based grammar as the one we are focusing on, to create tree structures without the proper feature equations is of little use.

experience has shown that feature equations are much harder to maintain correct and consistent in the grammar than the tree structures.


We generate 1200 trees (instead of 1008).

9 things are not as bad as they look: 206 of them are for passives related to multi-anchor trees, as we explain next.


Note that the figure shows only one possible hierarchy where the dimensions ordered by TOPIC, ZONE and MOOD.


From a viewpoint of category representation, the fact that the derived flat model enumerates all combinations among categories, makes the representation of a class be more precise than the class in multi-dimensional model.

from the viewpoint of relationship constraints in these models, the derived flat category model ignores the relationship among categories while the derived hierarchical model explicitly declares such relationship in a rigid manner, and the multi-dimensional model is a compromise between these two previous models.


This makes such nodes represent classes less imprecisely but there are more training data (documents) for these nodes.

nodes near leaves will have finer granularity and then have more precise representation but have less training data.


Similar to most of the parsers for many languages, our parser is based on words.

most other parsers for Japanese are based on a unique phrasal unit called a bunsetsu, a concatenation of one or more content words followed by some grammatical  function words.


In Example-Based MT based on analogical reasoning (Malavazos, 2000;Guvenir, 1998), the different parts are replaced by variables to generalize translation examples as shown in (1) of Figure 1.

the number of different parts of the two SL sentences must be same as the number of different parts of the two TL sentences.


However, it is difficult to acquire the translation rules that correspond to the lexicon level.

we have proposed a method of Machine Translation using Inductive Learning with Genetic Algorithms(GA-ILMT) (Echizenya et al., 1996).


As a result, the quality of the translation and the effective translation rate of our system is higher than other Rule-Based MT systems.

our system still does not reach the level of a practical MT system and requires more translation rules to realize the goal of a practical MT system.


We would choose the tree with higher confidence.

this is not possible in our case because weightings of the Minipar trees are not publicly available, and the shallow parser is a rule-based system without confidence information.


For instance, if £ appears 10 times in the corpus and (where¨is a normalizing constant).

if £ appears 200 times in the corpus and is a more frequent lemma combination, and should contribute a higher score to the paraphrase.


Since an ontology arranges words in a semantic hierarchy, it is possible for a word to appear in several different places in the hierarchy depending on its semantic sense.

words and concepts in a given language do not always translate cleanly into a second language; a word often has multiple translations, and they do not always share the same meanings.


This example is a good illustration of the strength and power of the cross-lingual 3 The original number of definitions chosen for the test set was higher.

upon inspection, it was found that a number had no corresponding WordNet synset and thus cannot be aligned.


Again, the phrasal word translations "move house", "change one's residence", "move to a better place", etc were filtered out, leaving the single word "move", which has a total of 16 senses as a verb in WordNet 1.7.

as the table shows, the algorithm correctly assigns the "change residence" sense of "move" to the HowNet definition, which is appropriate for the Chinese words it contains, which include " " (move house), " " (change one's dwelling), and " " (tear down one's house and move).


If one of the processes gives up its mission, the entire translation process also gives up and fails.

our model (sometimes) allows the transfer to give up generating the target language.


It would be a major problem in this design if there were many interaction loops between the paraphraser and the transfer, but we found that such worries are unwarranted in the current system.

it is necessary to be careful in this measure, since we need to add more functions to the paraphraser in order to avoid zero output.


It could be used, for example, to choose spleen neoplasms from concept class C0751521 as the translation of Milztumoren.

since such a distinction between the different lexicalizations of a given concept is beyond the scope of the current paper, we make the additional simplifying assumption that, given a concept class, the target word t is independent of the source word s, which leads to the simplified formula: The above equation views the thesaurus as a trellis linking source and target words.


Turning now to modules in grammar implementations, we see that similar to modules in ordinary software projects, grammar modules encode generalizations (functional units, property P2).

we argue below that (certain) grammar modules are not black boxes (whose internal structure is irrelevant, property P1), because these generalizations encode important linguistic insights.


The reader of the documentation would simply follow these links (which might be realized by hyperlinks).

6 a typical grammar rule calls many macros, and macros often call other macros.


A modularized grammar is "intensionally transparent", as we put it, and thus favours maintainability.

for casual users of the grammar, modularity may result in decreased readability.


According to , the analyzing time followed a quadratic curve.

our parser analyzes a sentence in linear time keeping a better accuracy.


Furthermore, to define a joint probability over the observation and state sequences, the generative approach needs to enumerate all the possible observation sequences.

in some tasks, the set of all the possible observation sequences is not reasonably enumerable.


Third, the generative approach normally estimates the parameters to maximize the likelihood of the observation sequence.

in many NLP tasks, the goal is to predict the state sequence given the observation sequence.


Traditionally, there are two existing approaches to resolve this problem: linear interpolation (Jelinek 1989) and back-off (Katz 1987).

these two approaches only work well when the number of different information sources is limited.


In our use of clustering we draw on an idea from Yaari (1997) who uses this technique as a first step in his hierarchical segmentation algorithm.

where Yaari uses a similarity measure based on word co-occurrence to decide which segments should be merged, we use a machine learnt model of segment relatedness.


Note, unlike most approaches that make use of lexical chains, we do not break a chain in two if too many sentences intervene between the individual chain elements; chains are continued as long as new elements can be found.

the algorithm keeps track of where in the text chain elements were found.


In our set of texts, the present tense was for instance used in a lot of places, sometimes to refer to events in the past, sometimes to refer to events that were going to happen at the time the text was published.

given the method we adopted, one could have expected better coherence results than finesse results.


Work in HPSG (Pollard and Sag, 1994;Ginzburg and Sag, 2000, inter alia) has instead focused on well-grounded structuring of grammatical information, most specifically in terms of type-inheritance hierarchies, although often at the cost of coverage and elegance (see Penn and Hoetmer 2003).

the respective foci of work in these frameworks are largely orthogonal, suggesting a great potential in combining them, as recent work by Villavicencio (2001) and Baldridge (2002) has demonstrated.


For instance, to build the NP happy dogs from Cleveland in HPSG lexically would generate a lexical NP dogs incompatible with the constraints on modifiers like happy (which haveN MOD values) and further would prevent the added quantifier to outscope the modifiers.

a phrasal approach misses the broader generalization that these constructions are lexically triggered (by particular noun classes/inflection) and again heterogeneously spreads out language particular grammatical information between the lexicon and phrasal rules.


The parsing methodology investigated here has previously been applied to Swedish, where promising results were obtained with a relatively small treebank (approximately 5000 sentences for training), resulting in an attachment score of 84.7% and a labeled accuracy of 80.6% (Nivre et al., 2004).

1 since there are no comparable results available for Swedish, it is difficult to assess the significance of these findings, which is one of the reasons why we want to apply the method to a benchmark corpus such as the the Penn Treebank, even though the annotation in this corpus is not ideal for labeled dependency parsing.


Under this worstcase analysis many simple concept classes are unlearnable.

in many situations it is more realistic to assume that there is some relationship between the concept and the distribution, and furthermore in general only positive examples will be available.


We compare it to a state in the hypothesis v. If this state is a representative of the same state in the target v, then L ∞ (Ŝ v , P q ) < µ/4 (by the goodness of the multisets), the triangle inequality shows that L ∞ (Ŝ u,σ ,Ŝ v ) < µ/2, and therefore the comparison will return true.

let us suppose that v is a representative of a different state q v .


Indeed we can deal with the previous objection in the same way if necessary by requiring the number of states in the generating PDFA to be bounded by a polynomial in the minimal number of states needed to generate the target language.

both of these limitations are unavoidable given the negative results previously discussed.


For example, automated scores are capable of distinguishing improved MT performance on easier texts or degraded performance on harder texts, so the automated scores also give information on whether one collection of texts is easier or harder than the other for an MT system: the complexity of the evaluation task is directly reflected in the evaluation scores.

there may be a need to avoid such sensitivity.


MT evaluation is harder than evaluation of other NLP tasks, which makes it partially dependent on intuitive human judgements about text quality.

automated tools are capable of capturing and representing the "absolute" level of performance for MT systems, and this level could then be projected into task-dependent figures for harder or easier texts.


Human evaluation results are available for all of the texts, with the exception of the news reports translated by System-2, which was not part of the DARPA 94 evaluation.

the human evaluation scores were collected at different times under different experimental conditions using different formulations of the evaluation tasks, which leads to substantial differences between human scores across different evaluations, even if the evaluations were done at the same time.


A similar tendency also holds for the system after dictionary update.

technically speaking the compared systems are no longer the same, because the dictionary update was done individually for each system, so the quality of the update is an additional factor in the system's performance -in addition to the complexity of the translated texts.


The retrieval procedure can be efficiently implemented by the techniques of clustering (Cranias et al., 1997) or using A* search algorithm on word graphs (Doi et al., 2004).

it still takes more cost to calculate Sim than P rob when the corpus is large.


newswire does not offer direct access to facts, events, and opinions; rather, journalists report what they have experienced, and report on the experiences of others.


By assigning the agents (and the corresponding dialogues) to one of two families we give ourselves the option of restricting user-led transitions between main and ancillary transactions.

the overall objective of our implementation is to maintain a high degree of flexibility in the manner in which the system reacts to unsolicited user utterances.


The PaymentExpert is identified as an appropriate payment handler, and is placed above AccommodationExpert on the ExpertFocusStack.

let us suppose that eliciting payment details first involves eliciting address details, and so the PaymentExpert in its turn asks the DomainSpotter to find it an agent specialising in address processing -in this case the AddressExpert.


For example, at the outset of an accommodation enquiry, the related service dialogue frame will not generally contain an explicitly linked payment frame.

the DomainSpotter is able to determine which agents can provide payment support, and so the system generates a number of potential discourse paths relating to payment.


Accounts of discourse structure vary greatly with respect to how many discourse relations they assume, ranging from two (Grosz & Sidner, 1986) to over 400 different coherence relations, reported in Hovy and Maier (1995).

hovy and Maier (1995) argue that taxonomies with more relations represent subtypes of taxonomies with fewer relations.


We adopted a sentence unit-based definition of discourse segments.

we also assume that contentful coordinating and subordinating conjunctions (cf.


Moreover, the attributes and values in a question "pair up" naturally to indicate equality constraints in SQL.

values may be paired with implicit attributes that do not appear in the question (e.g., the attribute 'cuisine' in "What are the Chinese restaurants in Seattle?"


PRECISE does return two interpretations a small percentage of the time.

even when restricted to returning a single interpretation, PRECISE-1 still achieved an impressive 89.1% accuracy (Table 1).


These models provide principled ways of including additional conditioning variables other than the preceding words, such as morphological or syntactic features.

the number of possible choices for model parameters creates a large space of models that cannot be searched exhaustively.


(Geutner, 1995;Ç arki et al., 2000;Kiecza et al., 1999)).

in all of these studies words were decomposed into linear sequences of morphs or morph-like units, using either linguistic knowledge or datadriven techniques.


For this reason, a context larger than that provided by a trigram is typically required, which quickly leads to data-sparsity.

to these approaches, factored language models encode morphological knowledge not by altering the linear segmentation of words but by encoding words as parallel bundles of features.


Therefore, when we write a report or an explanation on a newlydeveloped technology intended for readers including laypersons, it is very important to compose a title that will stimulate their interest in the technology.

technical specialists are not necessarily good at composing appealing titles, because it isn't clear what sort of titles will stimulate the interest of lay readers in the technology.


Several researches have been reported on title generation (Jin and Hauptmann, 2000) (Berger and Mittal, 2000) and readability of texts (Minel et al., 1997) (Hartley and Sydes, 1997) (Inui et al., 2003).

the researches on title gener-ation focus on generating a very compact summary of the document rather than composing an appealing title.


f are(A) ∧ airline(B) → on(A, B) meal(A) ∧ f light(B) → on(A, B) f light(A) ∧ day(B) → on(A, B) f light(A) ∧ airline(B) → on(A, B) It is an old observation that in order to choose the correct reading of an ambiguous sentence, we need a great deal of knowledge about the world.

the observation that disambiguation decisions depend on knowledge of the world can be made to cut both ways: just as we need a lot of knowledge of the world to make disambiguation decisions, so a given disambiguation decision can be interpreted as telling us a lot about the way we view the structure of the world.


Thus, the above pattern is converted to the following: It is now easier to understand the pattern as :'A person C who is elected succeeds a person E'.

it is still not straightforward how one can evaluate the usefulness of such patterns or indeed how one can incorporate the information they carry into a system for disambiguation or reasoning.


For the remainder of this paper, we will suppress up marking and write s up simply as s. Examples 13and 14show that prosodically marked and unmarked phrases can combine.

both of these partial derivations produce categories that cannot be combined further.


Using a best-first search strategy with the adjacency constraint we obtain the following alignment:  None of the alignment approaches described above produces the preferred reference alignment in our example using the given clue matrix.

simple iterative procedures come very close to the reference and produce acceptable alignments even for multi-word units, which is promising for an automatic clue alignment system.


Gold standards can be re-used for additional test runs which is important when examining different parameter settings.

recall and precision derived from information retrieval have to be adjusted for the task of word alignment.


Non-contiguous elements could also be identified using the same approach simply by removing the adjacency constraint.

this seems to increase the noise significantly according to experiments not shown in this paper.


Using the IBM translation models IBM-1 to IBM-5 (Brown et al., 1993), as well as the Hidden-Markov alignment model (Vogel et al., 1996), we can produce alignments of good quality.

all these models constrain the alignments so that a source word can be aligned to at most one target word.


Traditionally, coreference resolution is done by mining the reference relationships between NP pairs.

an individual NP usually lacks adequate description information of its referred entity.


For example, words of similar semantic types, such as company -government, tend to come up as distributionally similar, even though they are not substitutable in a meaning preserving sense.

many semantic-oriented applications, such as Question Answering, Paraphrasing and Information Extraction, do need to recognize which words may substitute each other in a meaning preserving manner.


This framework seems better than SVM to select best things.

it is well known that attachment ambiguity of PP is a major problem in parsing.


It seems that root-node finding is relatively easy and SVM worked well.

pp attachment is more difficult and SVM's behavior was unstable whereas preference Learning was more robust.


To give one example, the number of categories in the tag dictionary's entry for the word is is 45 (only considering categories which have appeared at least 10 times in the training data).

in the sentence Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group., the supertagger correctly assigns 1 category to is for β = 0.1, and 3 categories for β = 0.01.


There are two main reasons for this: (1) formalisations of information structure often use variants of higherorder logic to characterise its semantic impact (Krifka, 1993;Kruijff-Korbayova, 1998;Steedman, 2000), which limits the use of inference in practice (Blackburn and Bos, 2003); and (2) the effect of information structure on the compositional semantics of an utterance is rarely worked out in enough detail useful for computational implementation.

exploring information structure in spoken dialogue systems is becoming realistic now because of the recent advances made in text-to-speech synthesisers and automated speech recognisers -hence there is a growing need for computational implementations of information structure in grammar formalisms.


The directionality of the attributes of a functor category is marked by the features pre and post on its attributes rather than by the directionality of the slashes as it is done in CCG.

to CCG, UCG only uses forward and backward application as means for combining categories.


Otherwise it would be impossible to tell which item actually carries the accent for larger phrases such as married Manny H* LL% , where without the above mentioned constraint we could combine married and Manny first to form the unit married Manny, and only then combine this two word unit with the pitch accent.

this is not what we want, because this way we cannot determine any more which of the two words was accented.


Normally, a new edu is created from the begin position of the cue phrase to the end boundary of the NP.

this procedure may create incorrect results as shown in the example below: (1) [In 1988, Kidder eked out a $46 million profit, mainly][ because of severe cost cutting.]


Often-cited examples of such interactive robots that have a capability of communicating in natural language are the humanoid robot ROBOVIE (Kanda et al., 2002) and robotic museum tour guides like RHINO (Burgard et al., 1999) (Deutsches Museum Bonn), its successor MINERVA touring the Smithsonian in Washington (Thrun et al., 2000), and ROBOX at the Swiss National Exhibition Expo02 (Siegwart and et al, 2003).

dialogue systems used in robotics appear to be mostly restricted to relatively simple finite-state, query/response interaction.


a different JSAPI-compliant speech recogniser into our system is now a matter of changing a line in a configuration file.

building talking robots is still a challenge that combines the particular problems of dialogue systems and robotics, both of which introduce situations of incomplete information.


The previous system performs the antecedent estimation process for all the case frames of "orosu", and incorrectly estimates the antecedent of "wo" zero pronoun as "oroshigane" (grater) ‡ .

our proposed method deals with only the similar case frames to the cached "orosu (1)".


The English SemCor corpus has been manually annotated.

some annotation errors can be found in the texts (see Fellbaum et al., 1998, for SemCor taggers' confidence ratings).


In these cases the problem arises because in principle if the target expression is not a lexical unit it cannot be annotated as a whole.

each component of the free combination of words should be annotated with its respective sense.


This is not a problem so long as this knowledge is to be applied locally, in face-to-face communication with patients.

as a result of recent developments in technology, including telemedicine and internet-based medical query systems, we now face a situation where such dispersed, practical (human) knowledge does not suffice.


One such label is medicine; others are surgery and drug.

it was left undecided on what criteria terms should be selected as domain labels and what the relations among the relevant domains should be (arguably, surgery and drug should be included in the wider domain of medicine).


Currently, when asked to output terms associated with medicine, the browser returns some 504 nouns, verbs, and adjectives (both single words and phrases), representing some 270 different senses.

many cognate senses with clear medical uses are currently not labeled in this way.


Broad coverage unification-based deep parsers, however, unavoidably have problems meeting the very high accuracy and efficiency requirements needed for real-time dialog.

parsers based on lexicalized probabilistic context free grammars such those of Collins (1999) and Charniak (1997), which we call shallow parsers 1 , are robust and efficient, but the structural representations obtained with such parsers are insufficient as input for intelligent reasoning.


As we drop the chart size to 1500, the speed-up drops to just 1.4, as shown in Table 5.

we have improvements in accuracy using skeletons when we parse with low upper limits.


MMR bases this similarity score on word overlap and additional information about the time when each document was released, and thus can fail to identify repeated information when paraphrasing is used to convey the same meaning.

to these approaches, our model handles redundancy in the output at the same time it selects the output sentences.


In our experiments, we set out to investigate whether NTPC's operating parameters were overly simple, and whether more complex arrangements were necessary or desirable.

empirical evidence points to the fact that, in this problem of error correction in high accuracy ranges, at least, simple mechanisms will suffice to produce good results-in fact, the more complex operations end up degrading rather than improving accuracy.


More closely relevant to the experiments described here in, two of the best-performing three teams in the CoNLL-2002 Named Entity Recognition shared task evaluation used boosting as their base system (Carreras et al., 2002) (Wu et al., 2002).

precedents for improving performance after boosting are few.


The annotators used RST Tool (O'Donnell, 1997), which worked reasonably well for the purpose.

since we also have in our group an XML-based lexicon of German connectives at our disposal (Berger et al., 2002), why not use this resource to speed up the first phase of the annotation?


For each sentence in the given abstract, the corresponding source sentence is determined by combing the similarity score and heuristic rules.

it is known that bag-of-words representation is not optimal for short texts like single sentences (Suzuki et al., 2003).


From Table 1, we can calculate the WSK value as follows:  In this way, we can measure the similarity between two texts.

wSK disregards synonyms, hyponyms, and hypernyms.


However, we have to admit that the improvements are relatively small for single document data.

tree Kernel did not work well since it is too sensitive to slight differences.


The experimental results show that the best W is 2 or 3.

we could not find a consistently optimal value of d .


We make fairer precision evaluation in the next section.

since several related works make evaluation in this setting, we also present precision for reference.


In this example, we permit " (overseas study)".

we reject " (short-term overseas)" since it does not compose any constitutent in the compound word.


we cannot get same version of the corpus.


This seems natural because words are used as a processing unit in the Markov model-based method, and therefore much information about known words (e.g., POS or word bigram probability) can be used.

unknown words cannot be handled directly by this method itself.


Clearly, this evaluation is at a coarser level of granularity than that required for our final system.

we find it useful for the following reasons: Owing to the number and diversity of newsgroups on the Internet, we can perform controlled experiments where we vary the degree of similarity between newsgroups, thereby simulating discussions with different levels of relatedness.


Alternatively, one could count all occurrences of a word in a posting, which could be useful for constructing more detailed stylistic profiles.

at present we are mainly concerned with words that appear across postings.


This suggests that the signature words create undesirable overlaps between the clusters.

when filtering is used, the clustering procedure reaches its best performance with ¤ ¡ , where the performance is extremely good.


(Lewis, 1992;Dumais et al., 1998), or augmenting the standard BoW approach with synonym clusters or latent dimensions (Baker and Mc-Callum, 1998;Cai and Hofmann, 2003).

none of the more elaborate representations manage to significantly outperform the standard BoW approach (Sebastiani, 2002).


If we compare the best BoW run (using the linear kernel and tf × idf -weighting) and the best BoC run (using 5,000-dimensional vectors with the polynomial kernel and tf × idf -weighting), we can see that the BoW representations barely outperform BoC: 82.77% versus 82.29%.

if we only look at the results for the ten largest categories in the Reuters-21578 collection, the situation is reversed and the BoC representations outperform BoW.


The semantic category assigned to a noun holds the information used for this type of disambiguation.

4.2.4 Aggregation of synonymous expressions to disambiguation, aggregation of synonymous expressions is important to organize extracted sentiment units.


The recall was not so high, especially in the MT method, but according to our error analysis the recall can be increased by adding auxiliary patterns.

it is almost impossible to increase the precision without our deep analysis techniques.


Comparisons of automatic evaluation metrics for machine translation are usually conducted on corpus level using correlation statistics such as Pearson's product moment correlation coefficient or Spearman's rank order correlation coefficient between human scores and automatic scores.

such comparisons rely on human judgments of translation qualities such as adequacy and fluency.


The other potential problem for correlation analysis of human vs. automatic framework is that high corpus-level correlation might not translate to high sentence-level correlation.

high sentence-level correlation is often an important property that machine translation researchers look for.


This example illustrated that ROUGE-L can work reliably at sentence level.

lCS suffers one disadvantage: it only counts the main insequence words; therefore, other alternative lCSes and shorter sequences are not reflected in the final score.


According to Table 3, we find that shorter BLEUS has better correlation with adequacy.

correlation with fluency increases when longer ngram is considered but decreases after BLEUS5.


In evaluation of applications presupposing parsing, it is helpful to separate errors due to parsing from intrinsic errors.

one would also like to gauge the end-to-end performance of a system.


As stated in the previous section, utterances training are approximately 2.5 times faster than the test utterances.

they are expected to achieve a very low accuracy.


In this experiment, the number of states for a long vowel phoneme is varied from 3 to 6 states.

the numbers of states for the other phonemes are set to 3 states.


In a single document, there will be few sentences with the same content.

in multiple documents with multiple sources, there will be many sentences that convey the same content with different words and phrases, or even identical sentences.


Therefore, we believe that second type of extract is superior and thus we prepared the extracts in that way.

as stated in the previous section, with multiple document summarization, there may be more than one sentence with the same content, and thus we may have more than one set of sentences in the original document that corresponds to a given sentence in the abstract; that is to say, there may be more than one key datum for a given sentence in the abstract 5 .


Utilizing hard matching pattern rules could obtain precise results from test instances.

the approach is problematic in dealing with natural language text, such as news articles, which often exhibits great variations in both lexical and syntactic constructions.


In this paper, we aim to minimize the number of hand-tagged training instances needed to start the learning process by adopting a bootstrapping strategy such as that proposed in Riloff and Jones (1999).

to the existing work, we propose a weakly supervised IE framework which takes advantages of both soft and hard matching pattern rules in both the training and test phases.


One such solution might be a summary of that very email discussion.

it would be much more useful if the summary did not just tell the user what the thread is about.


2 Background: Email Threads The focus of this paper is on email discussions supporting a group decision-making process.

to studies on individual email usage (for an overview see: Ducheneaut and Bellotti, 2001), this research area has been less explored.


Occasionally, such discussions end with an online vote.

ducheneaut and Belotti do note that voting is relatively infrequent and our own experience with our email corpora tends to support this.


The set of group tasks facilitated by the email correspondence were: decision-making, information provision, requests for action and social conversation.

it is natural for the group to engage in multiple tasks.


They extract sentences based on the presence of subject line key words.

should the subject line not reflect the content of the thread, our method has the potential to extract the true discussion issue since it based on the responses of other participants.


For such threads, we attempt to extract the sentence participants respond to.

again, this may not be the best formulation of the issue.


The vector computed by the SVD centroid method provides information about the replies and accounts for word associations such as synonyms.

like the centroid method, this vector will include all topics discussed in the replies, even small digressions.


In this solution, we applied the issue detection algorithm to the reply email in question.

it turns out that most of the tagged responses occurred at the start of each reply email and a more complex approach was unnecessary and potentially introduced more errors.


When comparing the oracle methods which returned more than one sentence against the n=3 baseline, we found no significant difference in recall.

when comparing precision performance we found that the difference between the precision of Centroid method and the three oracles were significantly different compared to the baseline.


Derived from his supervised transformation-based tagger (Brill, 1992), UTBL uses information from the distribution of unambiguously tagged data to make informed labeling decisions in ambiguous contexts.

to the HMM taggers previously described, which make use of contextual information coming from the left side only, UTBL considers both left and right contexts.


where σ 2 k is the variance for feature dimension k. The variance can be feature dependent.

for simplicity, constant variance is often used for all features.


In addition, compared to simple models like n-gram language models (Teahan et al., 2000), another shortcoming of CRF-based segmenters is that it requires significantly longer training time.

training is a one-time process, and testing time is still linear in the length of the input.


Terminological processing has long been recognised as one of the crucial aspects of systematic knowledge acquisition and of many NLP applications (IR, IE, corpus querying, etc.).

term variation has been under-discussed and is rarely accounted for in such applications.


morphological) is based on stemming: if two term forms share a stemmed representation, they are considered as mutual variants (Jacquemin and Tzoukermann, 1999;.

stemming may result in ambiguous denotations related to "overstemming" (i.e.


Still, using only the patterns from Table 3, we have correctly extracted 35.76% of all GENIA coordinated terms, with more than a half of all suggested candidates being found among those that appeared exclusively in coordinations.

these patterns also generated a number of false coordination expressions, and consequently a number of false term candidates.


And using just transliteration information alone, 9 Chinese words have their correct English translations at rank one position.

using our method of combining both sources of information and setting M = ∞, 19 Chinese words (i.e., the first 22 Chinese words in Table 3 except 巴佐亚,坩埚,普利法) have their correct English translations at rank one position.


For example, the work of (Al-Onaizan and Knight, 2002a;Al-Onaizan and Knight, 2002b;Knight and Graehl, 1998) used only the pronunciation or spelling of w in translation.

the work of (Cao and Li, 2002;Fung and Yee, 1998;Rapp, 1995;Rapp, 1999) used only the context of w to locate its translation in a second language.


On the other hand, the work of (Cao and Li, 2002;Fung and Yee, 1998;Rapp, 1995;Rapp, 1999) used only the context of w to locate its translation in a second language.

our current work attempts to combine both complementary sources of information, yielding higher accuracy than using either source of information alone.


This may look linguistically counter-intuitive.

(Koehn et al 2003) found that it is actually harmful to restrict phrases to constituents in parse trees, because the restriction would cause the system to miss many reliable translations, such as the correspondence between "there is" in English and "es gibt" ("it gives") in German.


5(c), both of these rules can be applied to parts of the tree.

they cannot be used at the same time as they translate same to different words and place them on different location.


A separate generation module, which often involves some target language grammar rules, is used to linearize the words in the target parse tree.

our transfer rules specify linear order among nodes in the rule.


When the alignment is known, we can efficiently determine the optimal translation with that alignment.

when the translation is known, we can efficiently determine a better alignment.


IE is known to have established a now widely accepted linguistic architecture based on cascading automata and domain-specific knowledge (Appelt et al, 1993).

several studies have outlined the problem of the definition of the resources.


The process is weakly supervised since the analyst only has to provide one example to the system.

we observed that the quality of the acquisition process highly depends from this seed example, so that several experiments has to be done for the acquisition of an argument structure, in order to be sure to obtain an accurate coverage of a domain.


They sometimes obtain unclear conclusions about the reason of the performances of the different algorithms (for example, comparing Jiang and Conrath's measure (1997) with Lin's one (1998): "It remains unclear, however, just why it performed so much better than Lin's measure, which is but a different arithmetic combination of the same terms").

the authors emphases on the fact that the use of the sole hyponym relation is insufficient to capture the complexity of meaning: "Nonetheless, it remains a strong intuition that hyponymy is only one part of semantic relatedness; meronymy, such as w h e e l -c a r, is most definitely an indicator of semantic relatedness, and, a fortiori, semantic relatedness can arise from little more than common or stereotypical associations or statistical co-occurrence in real life (for example, p e n g u i n -A n t a r c t i c a ; birthday-candle; sleep-pajamas)".


The activation measure d _ is equal to the mean of the weight of each NCA calculated from A and B : Please, refer to (Dutoit and Poibeau, 2002) for more details and examples.

this measure is sensitive enough to give valuable results for a wide variety of applications, including text filtering and information extraction (Poibeau et al., 2002).


We estimate that generally 25% of the acquired pattern should be rejected.

this validation process is very rapid: a few minutes only were necessary to check the 31 proposed patterns and retain 25 of them.


Additionally, by reducing the size of descriptions, Cyclone can be used with mobile devices, such as PDAs.

while Cyclone includes various types of terms, such as technical terms, events, and animals, the required set of viewpoints can vary depending the type of target terms.


To resolve this problem, zero pronoun detection and anaphora resolution can be used.

due to the rudimentary nature of existing methods, we use hand-crafted rules to complement simple sentences with the subject.


A simple sentence that matches with patterns for multiple viewpoints is classified into every possible group.

the pattern-based method fails to classify the sentences that do not match with any predefined patterns.


It may be argued that an existing hand-crafted encyclopedia can be used as the standard summary.

paragraphs in Cyclone often contain viewpoints not described in existing encyclopedias.


Third, the VBS method outperformed the lead method in terms of the coverage, excepting the case of "#Reps=1" focusing on the 12 viewpoints by annotator B.

in general the VBS method produced more informative summaries than the lead method, irrespective of the compression ratio and the annotator.


A low coverage for the synonym is partially due to the fact that synonyms are often described with parentheses.

because parentheses are used for various purposes, it is difficult to identify only synonyms expressed with parentheses.


For example, in TREC QA track, definition questions are intended to provide a user with the definition of a target item or person (Voorhees, 2003).

while the expected answer for a TREC question is short definition sentences as in a dictionary, we intend to produce an encyclopedic text describing a target term from multiple viewpoints.


Thórisson's original algorithm (Thórisson, 1994) takes into account these relations as well as positional relations of objects when calculating similarity between objects to generate groups.

if we generate groups using multiple relations simultaneously, the assumption used in Step 1 of our algorithm that any pair of groups in an output list do not intersect without a subsumption relation cannot be held.


We assumed that all participants in a conversation shared the same reference frame.

when we apply our method to conversational agent systems, e.g., (Cavazza et al., 2002;Tanaka et al., 2004), reference frames must be properly determined each time to generate referring expressions.


Experiments by Daumé et al (2002) and the parsing work of Charniak (2000) and others indicate that further lexicalization may yield some additional improvements for ordering.

the parsing results of Klein & Manning (2003) involving unlexicalized grammars suggest that gains may be limited.


Language model based IR system proposed in recent 5 years has introduced the language model approach in the speech recognition area into the IR community and improves the performance of the IR system effectively.

the assumption that all the indexed words are irrelative behind the method is not the truth.


The maximum of the average precision is reached when three MeSH terms are selected per query (0.1925), but we can notice that selecting only two terms is as effective (0.19).

selecting the unique top returned term is not sufficient (average precision is below 0.145), and adding more than three terms smoothly degrade the precision, so that with 25 terms, precision falls below 0.15.


In a given CTS, a past perfect clause should precede the event described by a simple past clause.

the order of two events in CTS does not necessarily correspond to the order imposed by the interpretation of the connective (Dorr, 2002).


Assuming event classes are independent of temporal indicators given c , we have:   A Naïve Bayesian Classifier assumes strict independence among all attributes.

this assumption is not satisfactory in the context of temporal relation determination.


This model is called Ungrouped Model (UG).

as illustrated in table 1, the temporal indicators play different roles in building temporal reference.


One may think that if back-transliteration were done precisely, those variants would be backtransliterated into one word, and they would be recognized as variants.

backtransliteration is known to be a very difficult task (Knight and Graehl, 1997).


Practically, these words are identified as different words by using a dictionary.

when using only contextual similarity, these words would be judged as variants.


The results showed that detection of short word variants is very difficult, and a dictionary raised the precision for such short words.

contextual similarity did not contribute as expected to the detection of orthographic variants.


For instance, noisy-channel model (NCM) (Virga et al., 2003;Lee et al., 2003), HMM (Sung et al., 2000), decision tree (Kang et al., 2000), transformation-based learning (Meng et al., 2001), statistical machine transliteration model (Lee et al., 2003), finite state transducers (Knight et al., 1998) and rule-based approach (Wan et al., 1998;Oh et al., 2002).

it is observed that the reported transliteration models share a common strategy, that is: 1) To model the transformation rules; 2) To model the target language; 3) To model the above both; the modeling of different knowledge is always done independently.


By eliminating the potential imprecision introduced through a multiple-step phonetic mapping in the phoneme-based approach, DOM is expected to outperform.

to phoneme-based approach, DOM is purely datadriven, therefore can be extended across different language pairs easily.


As for SCF acquisition, the cooccurrence of one predefined SCF scf i with one verb v is the relevant primitive event, and the concerned probability is p(v|scfi) here.

the aim of filtering is to rule out those unreliable hypotheses, so it is the probability that one primitive event doesn't occur that is often used for SCF hypothesis testing, i.e.


However, it seems that inappropriate translation equivalents were often ranked high by the CS method.

referring to the representative associated words enables the results of the RAW method to be judged as appropriate or inappropriate.


The RAW method maximizes the F-measure, i.e., harmonic means of recall and precision, when the threshold for the ratio of associated words is set at 4%; the recall, precision, and F-measure are 92%, 80%, and 86%, respectively.

the CS method maximizes the F-measure when N is set at nine; the recall, precision, and F-measure are 96%, 72%, and 82%, respectively.


• The RAW method assumes that a target word has more than one sense, and, therefore, it is effective for polysemous target words.

contextual similarity is ineffective for a target word with two or more senses occurring in a corpus.


Jing (Jing 00) developed a method to remove extraneous phrases from sentences by using multiple sources of knowledge to decide which phrases could be removed.

while this method exploits a simple model for sentence reduction by using statistics computed from a corpus, a better model can be obtained by using a learning approach.


One way to solve this problem is to select multiple actions that correspond to the context at each step in the rewriting process.

the question that emerges here is how to determine which criteria to use in selecting multiple actions for a context.


We suppose the topical clustering could not prove the merits with this test collection because the collection consists of relevant articles retrieved by some query and polished well by a human so as not to include unrelated articles to a topic.

the proposed method (PO) improved chronological ordering much better than topical segmentation.


Tukey test revealed that RO was definitely the worst with all metrics.

spearman's rank correlation τ s and Kendall's rank correlation τ k failed to prove the significant difference between CO, PO and HO.


All of these approaches assume events occur in text in certain patterns.

this assumption may not be completely correct and it limits the syntactic information considered by these approaches for finding events, such as information on global features from levels other than deep processing.


For slot filler detection, several classifiers were trained to find names for each slot and there is no correlation among these classifiers.

entity slots in events are often strongly correlated, for example the PER_IN and POST slots for management succession events.


She used an iterative process to semi-automatically learn patterns.

a corpus of 20MB words yielded only 400 examples.


CBC (Clustering by Committee) proposed by Pantel and Lin (2002) achieves high recall and precision in generating similarity lists of words discriminated by their meaning and senses.

such clustering algorithms fail to name their classes.


The former attribute greatest weights to very rare context words, some of which seem rather informative ( knock_by, climb_of, see_into), some also appear to be occasional collocates (remand_to, recover_in ) or parsing mistakes (entrust_to, force_of).

the latter encourage frequent context words.


Finding the four nearest neighbors for each word in the collection, we calculated the average minimum similarity score that a pair of words must have in order to be considered related.

since words vary a lot in terms of the amount of corpus data available on them, the average similarity threshold might be inappropriate for many words.


cosine and Jaccard measure, Euclidean distance and scalar product.

his measures were taken to find the most similar vector for a given word in order to automatically identify word translations.


Applying a two-step technique (translation rules and fuzzy n-gram matching), they achieved 81.1% of average precision in a Spanishto-English context covering biomedical words only.

their evaluation metrics considerably differed from ours, since they considered multiple hypotheses.


In back transliteration, an English letter or string is chosen to correspond to a katakana character or string.


Since it is hard to construct a set of predefined names by hand, usually some corpus based approaches are used for building such taggers.

as Zipf's law indicates, most of the names which occupy a large portion of vocabulary are rarely used.


For each word w which appeared in newspaper A, we got the document frequency at date t: where df A (w, t) is the number of documents which contain the word w at date t in newspaper A.

the normalization constant N A (t) is the number of all articles at date t. comparing this value between two newspapers directly cannot capture a time lag.


It can be seen that the fact that the executives are leaving and the name of the organisation are listed in the first sentence.

the names of the executives and their posts are listed in the second sentence although it does not mention the fact that the executives are leaving these posts.


In an effort to overcome this brittleness machine learning methods have been applied to port systems to new domains and extraction tasks with minimal manual intervention.

some IE systems using machine learning techniques only extract events which are described within a single sentence, examples include (Soderland, 1999;Chieu and Ng, 2002;Zelenko et al., 2003).


Then we ran English-QDIE system to get the extraction patterns, which are used to extract the entities by pattern matching.

one can first translate the scenario description into the source language and use it for the monolingual QDIE system for the source language, assuming that we have access to the tools for pattern acquisition in the source language.


As we shall demonstrate, the errors introduced by the MT system impose a significant cost in extraction performance both in accuracy and coverage of the target event.

if basic linguistic analysis tools are available for the source language, it is possible to boost CLIE performance by learning patterns in the source language.


Such problems, of course, may make it hard to detect Named Entities and get a correct dependency tree of the sentence.

translation of names is easier than translation of contexts; the MT system can output the transliteration of an unknown word.


This means that, as in face-to-face spoken dialog, the email thread as a whole is a collaborative effort with interaction among the discourse participants.

unlike spoken dialog, the discourse participants are not physically co-present, so that the written word is the only channel of communication.


These sentence-shortening approaches have been evaluated by comparison with human-shortened sentences and have been shown to compare favorably.

the use of sentence shortening for the multi-document summarization task has been largely unexplored, even though intuitively it appears that sentence-shortening can allow more important information to be included in a summary.


The machine summaries contain on average one parenthetical every 3.3 sentences.

human summaries contain only one parenthetical unit per 8.9 sentences on average.


the best precision is always obtained for short contexts ranging from ±1 to ±5 words.


Most current research uses machine learning techniques (Li and Takeuchi, 1997;Murata et al., 2001;Takamura et al., 2001), has achieved good performance.

as supervised learning methods require word sense-tagged corpora, they often suffer from data sparseness, i.e., words which do not occur frequently in a training corpus can not be disambiguated.


It is similar to the ordinary Naive Bayes model for WSD (Pedersen, 2000).

we assume that this model can not be trained for low frequency words due to a lack of training data.


In general, two or more hypernyms can be extracted from a definition sentence, when the definition of a sense consists of several sentences or a definition sentence contains a coordinate structure.

for this work we extracted only one hypernym for a sense, because definitions of all senses in the EDR concept dictionary are described by a single sentence, and most of them contain no coordinate structure.


training a WSD classifier from an unlabeled data set.

our approach is to use a machine readable dictionary in addition to a corpus as knowledge resources for WSD.


In the following, we call this method the Algorithm for Hyponymy Relation Acquisition from Itemizations (AHRAI).

the method we propose in this paper is called Hyponym Extraction Algorithm from Itemizations and Headings (HEAIH).


It will then obtain that "Toyota" is a hyponym of "car company" from document (A) in the figure, while it finds that "Toyota" is a hyponym of "car" from (B).


One may think that, for instance, she can use the distance between an itemization and (candidates of) its heading in the HTML file as a clue for finding the correspondence.

we empirically show that this is not the case.


Disagreement between human annotators and uncertainty about the interpretation of annotation guidelines may also lead to an element of randomness in the evaluation.

even significant results cannot be generalised to a different type of collocation (such as adjective-noun instead of PP-verb), different evaluation criteria, a different domain or text type, or even a source corpus of different size, as the results of  show.


Both observed precision values are consistent with an average precision π A = π B in the region of overlap, so that the observed differences may be due to random variation in opposite directions.

this conclusion is premature because the two rankings are not independent.


The collection of clusters is mainly presented to the users as a flat and independent list of clusters.

as we realised that some of the clusters are more related than others (e.g.


on average 1.68 historical links per major news cluster.

for 42 of the 136 major news clusters, the system did not find any related news clusters with a similarity of 15% or more.


The second and third column show the influence of the words (or word combinations) by themselves, which is extremely low.

when examining all patterns containing these words, the fourth and fifth columns, their usefulness becomes visible.


It is both important, because there is strong demand for all kinds of computer support for health care and clinical services, which aim at improving their quality and decreasing their costs, and challenging -given the miracles of medical sublanguage, the various text genres one encounters and the enormous breadth of expertise surfacing as medical terminology.

the development of human language technology for written language material has, up until now, almost exclusively focused on newswire or newspaper genres.


As shown in   bigram types is also reflected in the three-part distribution in Table 3: The number of POS trigrams occurring less than ten times is almost one third less in MED than in NEGRA or in NEWS; similarly, but less pronounced, this can be observed for POS bigrams.

the number of trigram types occurring more than 1000 times is even higher for MED, and the number of bigram and unigram types is about the same when scaled against the total number of types.


Though such noun t i may be an important noun for document d, it may be an irrelevant noun for document set S. Hence, noun t i that is assigned small entropy value should not be extracted as a relevant keyword.

a noun that appears uniformly in each document contained in document set S has a large entropy value.


Undoubtedly, internet texts have formed a very large and easilyaccessible corpus.

chinese texts in internet are not segmented so it is not costeffective to use them.


In table 6, the precision of 80.23% is slightly better than 79.96% of the 20-word condition, and just 1% lower than that of the 40-word condition.

the recall drastically increases from 45.56%, or 59.57% under the 40-word condition, to 85.03%.


≥ Table 8: Morpheme-based and word-based recall of high-frequency and low-frequency words The results showed that high-frequency words could be largely extracted by the algorithm with both morphemes (99.80% recall) and words (89.45% recall).

paradigm words gave 26.55% recall of low-frequency words, whereas paradigm morphemes gave 67.66%.


The efficient application of Turney's algorithm with help of colossal corpus like hundred-billionword corpus is matched by the ready availability of internet texts.

the same convenience is not available to Chinese because of the heavy cost of word segmentation.


Further, the second approach is clearly advantageous when one wishes to apply distributional similarity methods in a particular application area.

it is not at all obvious that one universally best measure exists for all applications (Weeds and Weir, 2003).


The value obtained (0.0525) is disappointing since it is not statistically significant (the probability of this value under the null hypothesis of "no correlation" is 0.3).

2 Haspelmath (2002) notes that a compositional collocation is not just similar to one of its constituents -it can be considered to be a hyponym of its head constituent.


One limitation of the corpus-based translation knowledge acquisition approach is that the techniques of translation knowledge acquisition heavily rely on availability of parallel/comparative corpora.

the sizes as well as the domain of existing parallel/comparative corpora are limited, while it is very expensive to manually collect parallel/comparative corpora.


Cao and Li (2002) restricted candidate bilingual compound term pairs by consulting a seed bilingual lexicon and requiring their constituent words to be translation of each other across languages.

in the framework of bilingual term correspondences estimation of this paper, the computational complexity of enumerating translation candidates can be easily avoided with the help of cross-language retrieval of relevant news texts.


He uses dynamic programming to break tree pairs into pairs of aligned elementary trees, similar to DOT.

he aims to estimate a translation model from unaligned data, whereas we wish to align our data off-line.


Results over the full set of output translations, summarised in Figure 9, show that using the manually linked fragment base results in significantly better overall performance at all link depths (LD1 -LD4) than using the automatic alignments.

both metrics used assign score 0 in all instances where no translation was output by the system.


In terms of Bleu scores, translations produced using manual alignments score slightly better at all depths.

as link depth increases the gap narrows consistently and at depth 4 the difference in scores is reduced to just 0.0125.


For example, there are relatively few instances of 'D→the' aligned with 'D→le/la/l'/les' in the automatic alignment compared to the manual alignment.

we achieve 10% less coverage when translating using the automatic alignments.


All of these approaches annotate examples by mean of a pair of analyzed structures, one for each language sentence, where the correspondences between inter levels of source and target structures are explicitly linked.

we found that these approaches require the bilingual examples that have 'parallel' translations or 'close' syntactic structures (Grishman, 1994), where the source sentence and target sentences have explicit correspondences in the sentences-pair.


We have tried to exploit the information about multiwords contained in the Collins bilingual dictionary.

it is well known that dictionaries contain only a small part of multiwords actually used in language.


In fact, KNOWA correctly aligns 2-boy with 5-bambino, and 5-dog with 2-cane, even if the English and Italian nouns are not in the same position in the respective sentences, thanks to a search in the Italian word window.

kNOWA would also align 1-the with 1-il, and 4-the with 4-al.


However, not all nouns have this characteristics.

certain function words, for instance conjunctions, may be involved in a one-to-one potential correspondence relation.


EuroParl includes texts in 11 European languages, automatically aligned at the sentence level, whereas EuroCor includes only a part of the texts in EuroParl and only for English and Italian.

multiSemCor is a reference English/Italian corpus being developed at ITC-irst, including SemCor (part of the Brown Corpus) and its Italian translations.


Most spoken dialogue systems that have been developed, such as airline information systems (Levin et al., 2000;Potamianos et al., 2000;San-Segundo et al., 2000) and train information systems (Allen et al., 1996;Sturm et al., 1999;Lamel et al., 1999), are categorized here.

it is not feasible to define such keywords in retrieval for operation manuals (Komatani et al., 2002) or WWW pages, where the target of retrieval is not organized and is written as natural language text.


In general, dependencies in Japanese written text do not cross.

dependencies in spontaneous speech sometimes do.


One method based on machine learning, a method based on maximum entropy models, has been proposed by Reynar and Ratnaparkhi (Reynar and Ratnaparkhi, 2000).

the target in their study was written text.


This parameter can again be used for basic tasks such as POS-tagging: Adjective-noun ambiguity is notoriously the most difficult one to solve, and the ordering restrictions on the classes of adjectives can help to reduce it.


It could seem that the semantic classes established for the second parameter amount to morphological classes: not derived (basic adjectives), denominal (object adjectives), and deverbal (event adjectives).

although there is indeed a certain correlation between morphological class and semantic class, we claim that morphology is not sufficient for a reliable classification because it is by no means a one-to-one relationship.


This suggests that this judge is an outsider and that the level of expertise needed for humans to perform this kind of classification is quite high.

there are too few data for this suspicion to be statistically testable.


But it will not predict very well certain locally regular words like of, the etc whose main role is to support the syntactic structure in a sentence.

n-gram language models are able to model them well because of maximum likelihood estimation from training corpus and various smoothing techniques.


This relative increase is mainly due to poor modeling of function words in the LSA-space.

for SELSA, we can observe that its perplexity of 36.37 is less than 50.39 value in the case of knowledge about content/function words.


Using N -best could bear computing burden.

our experiments have shown a smaller N seems to be adequate to achieve most of the translation improvement without significant increasing of computations.


In case of Japanese text analysis, (Murata et al., 1999) proposed a method of utilizing "N m no N h " phrases for indirect anaphora resolution of diverse relationships.

they basically used all "N m no N h " phrases from corpora, just excluding some pre-fixed stop words.


Furthermore, sometimes a definition is too specific or detailed, and the example phrases can adjust it properly, as in the example of hisashi in Table 2.

a simple method that just collects and clusters "N m no N h " phrases (based on some similarity measure of nouns) can not construct comprehensive nominal case frames, because of polysemy and multiple obligatory cases.


The algorithm scans text from left to right and selects the longest match with a dictionary entry at each point, in a greedy fashion.

longest possible words may not comply with the actual meanings.


This was because Method p&s couldn't select two correct variant pairs when the penalties were 1 and 2.

the precision of Method p&s was 16.2% higher.


Pantel and Lin (2002) demonstrate that it is possible to cluster the neighbours into senses and relate these to WordNet senses.

we use the distributional similarity scores of the neighbours to rank the various senses of the target word since we expect that the quantity and similarity of the neighbours pertaining to different senses will reflect the relative dominance of the senses.


The resulting semantic formalism is very similar to the type-theoretic language L λ (Dowty et al., 1981).

we merely use the lambdacalculus as a tool for constructing semantic representations, rather as a formal tool for modeltheoretic interpretation.


In Figure 3, for example, there are different potential standards that would admit either both squares or neither square as small.

we can rule out these candidate standards in interpreting (3).


In order to perform effective search, we employ question transformation to get effectual web queries.


Given the excellent results posted by the best systems and an adequate performance attained even by some entry-level system, we believe that the process of factoid question answering is now fairly well understood (Harabagiu et al., 2002;Hovy et al., 2000;Prager at al., 2001, Wu et al., 2003.

to a factoid question, an analytical question has a virtually unlimited variety of syntactic forms with only a loose connection between their syntax and the expected answer.


With the optimal choice of the four words, we get results that are similar to those of Pantel and Lin: their precision is equal to 60.8 with an average number of words defining a sense equal to 14.

table 4 shows that the words selected on the basis of their number of strong links are not strongly linked in WordNet (according to Lin's measure) to their target word.


These compounds are undoubtedly included for a reason, yet the idea that literal compounds might actually be essential to WordNet's usefulness may strike some as heretical on at least two fronts: first, the lexicon is a finite resource, while the space of compounds is potentially infinite; and at any rate, literal compounds can be created as needed from purely compositional principles (Hanks, 2004).

these retorts are valid only if we view WordNet as a dictionary, but of course it is much more than this.


We can transform this into another concept M'-H by replacing M with any M' for which: This formulation may suggest a large range of values of M'.

these candidates can be sorted by A rel (M, M'), which estimates the probability that a given M'-H will later be validated as useful.


It follows then that the novel compounds apple-pizza, chocolate-pizza, taco-pizza, and curry-pizza will all be internally validated as meaningful (if not necessarily enjoyable) varieties of pizza.

the external validation set for a compound M-H is the set of distinct documents that contain the compound term "M H", as acquired using a web search engine.


These possibilities can be diminished by seeking a large enough sample set, but this has the effect of setting the evidential bar too high for truly creative compounds.

another solution lies in the way that the results of external validation are actually used, as we shall later see.


Experiments have also shown that the use of menu-based justifications help students learn with greater understanding over a tutor that does not ask for justifications (Aleven and Koedinger 2002).


there are cases when different words are used as synonyms only in certain contexts.


chunks and named entities) besides the words within the questions.

the main disadvantage of relying on semantic analyzers, named entity taggers and the like, is that for some languages these tools are not yet well developed.


For instance Zhang and Sun Lee (Zhang and Lee, 2003) reported an accuracy of 90% for English questions, while Li and Roth (Li and Roth, 2002) achieved 98.8% accuracy.

they used a training set of 5,500 questions and a test set of 500 questions, while in our experiments we used for training 405 for each 45 test questions (10-fold-cross-validation).


Ideally, we would like to compare our system with other summarizers.

due to the unavailability of other summarization systems to perform the same task, we designed three baseline methods, namely lead-based, randomly-selected, and random-lead-based, to generate summaries for performance comparison, which were also adopted by (Brandow et al.


Several parsers have been implemented in various grammar formalisms and empirical evaluation has been reported: LFG (Riezler et al., 2002;Cahill et al., 2002;Burke et al., 2004), LTAG (Chiang, 2000), CCG (Hockenmaier and Steedman, 2002b;Clark et al., 2002;Hockenmaier, 2003), and HPSG (Miyao et al., 2003;Malouf and van Noord, 2004).

their accuracy was still below the state-of-theart PCFG parsers (Collins, 1999;Charniak, 2000) in terms of the PARSEVAL score.


Since deep parsers can output deeper representation of the structure of a sentence, such as predicate argument structures, several studies reported the accuracy of predicateargument relations using a treebank developed for each formalism.

resources used for the evaluation were not available for other formalisms, and the results cannot be compared with each other.


However, lexical alternations and arbitrariness of assignments of argument labels will be a problem when we directly compare the output of an HPSG parser with the PropBank.

we can see that the remaining disagreements are about the labels of argument labels.


out of the 400 samples, they agreed only on 294 for action-effect and 297 for action-means.

a closer look at the results revealed that the judgements of the one annotator were considerably but very consistently more tolerant than the other.


Mapping verb instances to their VN classes has been proven useful for several NLP tasks.

verbs are polysemous with respect to their VN classes.


The similarity to WSD suggests that our task might be solved by WN sense disambiguation followed by a mapping from WN to VN.

good results are not to be expected, due to the medium quality of today's WSD algorithms and because the mapping between WN and VN is both incomplete and many-to-many 7 .


We follow common practice and use the standard reference Dirichlet prior, which is uniform on θ, such that α j = 1 for all j.

to the model above, a hierarchical sampling model assumes thatθ varies between documents, and has distribution which depends upon parameters η.


Thus p(d|α) (using the standard α notation for Dirichlet parameters) has distribution: Maximum likelihood estimates for the α are difficult to obtain, since the likelihood for α is a function which must be maximised for all components simultaneously, leading some authors to use approximate distributions to improve the tractability of maximum likelihood estimation (Elkan, 2006).

we reparameterise the Dirichlet compound multinomial, and estimate some of the parameters in closed form.


To reliably estimate variance (and arguably rate as well) would require words to occur a sufficient number of times.

this section will demonstrate that two of the datasets have many words which do not occur with sufficient frequency to estimate parameters, and in those two the linear SVM's performance is more comparable.


We attribute this to the capacity of the proposed model to capture aspects of word behaviour be-yond a simpler model.

in cases where the data contain many infrequent words and the tendency to reuse words is relatively low, defaulting to a linear classifier (either the multinomial for a generative classifier, or preferably the linear SVM) increases performance relative to a more complex model, which cannot be fit with sufficient precision.


The eigenvalue decomposition of B now incorporates this extra term information provided in D 1 , and the eigenvectors show stronger correspondence between those terms indicated.

with each term aligned with one or more other terms, the row and column norms of D 1 are unequal, which means that some terms may be biased to appear more heavily in the eigenvectors.


Other distributions could be given, e.g., one which gives more mass to adjunct categories such as (S\NP)\(S\NP) than to ones which are otherwise similar but do not display such symmetry, like (S/NP)\(NP\S).

the most important thing for present purposes is that simpler categories are more likely than more complex ones.


This property of CCG of supporting multiple derivations of the same analysis has been termed spurious ambiguity.

the extra constituents are anything but spurious: they are implicated in a range of CCG (along with other forms of categorial grammar) linguistic analyses, including coordination, long-distance extraction, intonation, and incremental processing.


Furthermore, using the lexical category distribution (ΨΛ) to create the transition initialization provides a better starting point than the uniform one (ΨU), espe- 8 CCGbank actually corrects many errors in the Penn Treebank, and does not suffer as much from mistagged examples.

there were two instances of an ill-formed category ((S[b]\NP)/NP)/ in wsj 0595 for the words own and keep.


This result may seem counterintuitive since neighbors provided by a semantic resource are based on expert knowledge and are often more accurate than those obtained automatically.

semantic resources like WordNet are designed to be as general as possible without a specific corpus or domain in mind.


Also note that improvements in supervised methods can be expected to automatically translate to improvements in unsupervised  Interestingly, label propagation performed relatively poorly on the manually labeled data.

it ranks highly when using the automatic labels.


Finally we have adopted Tsovaltzi and Karagjosova's top-level structure, with Task as a dimension.

we observe that it would be equally valid and more in keeping with the original DAMSL categorisation of utterance functions to make use of the existing Task sub-category of the Info-level dimension.


There are many supervised learning methods for training high-performance dependency parsers (Nivre et al., 2007), if given sufficient labeled data.

the performance of parsers declines when we are in the situation that a parser is trained in one "source" domain but is to parse the sentences in a second "target" domain.


The CoTrain system was similar to the learning scheme described by Sagae and Tsujii (2007).

we did not use the same parsing algorithms as the ones used by Sagae and Tsujii (2007) Table 5: The results of several adaptation methods with CKIP trained a forward parser (same as our baseline system) and a backward parser.


The best results here, for n=4, are essentially no better on average than those obtained with standard LSA.

averaging across languages obscures the fact that results for Arabic have significantly improved (for example, where Arabic documents are used as queries, MP5 is now 0.6205 instead of 0.4456).


(2004) present an end-to-end paraphrasing system inspired by phrase-based machine translation that can both acquire paraphrases and use them to generate new strings.

their model is limited to lexical substitution -no reordering takes place -and is lacking the compression objective.


The great advantages of such systems are their robustness and efficient processing, which make them highly suitable for real-life grammar and style checking applications.

since shallow modules usually cannot provide a full syntactic analysis, the coverage of these systems is limited to error types not requiring a broader (nonlocal) syntactic context for their detection.


The passive sentences found by Checkpoint are actually passive sentences.

these were not annotated as passives, because the annotators were told to annotate only those stylistic errors for which a paraphrase was possible.


Obviously, this fact has severely hampered the state-of-the-art of advanced NLP applications.

the Princeton WordNet (WN) is by far the most widely-used knowledge base (Fellbaum, 1998).


As expected, each semantic resource has different volume and accuracy figures when evaluated in a common and controlled framework (Cuadros and Rigau, 2006).

not all these large-scale resources encode semantic relations between synsets.


The algorithm finishes when no more pending words remain in P. Algorithm 1 SSI-Dijkstra Algorithm SSI (T: list of terms) for each {t ∈ T } do Initially, the list I of interpreted words should include the senses of the monosemous words in W, or a fixed set of word senses 5 .

when dis-ambiguating a TS of a word sense s (for instance party#n#1), the list I already includes s. In order to measure the proximity of one synset to the rest of synsets of I, we use part of the knowledge already available to build a very large connected graph with 99,635 nodes (synsets) and 636,077 edges.


Note that in WSD evaluation frameworks, this is a very basic baseline.

in our evaluation framework, this "WSD baseline" could be considered as an upper-bound.


As expected, KnowNet-5 obtain the lower results.

it performs better than WN (and all its extensions) and spBNC.


One simple way of verifying the latter case is by looking at the number of senses assigned to the prepositions by a resource  Table 5: Confusion matrix for L1 data -prepositions such as the Oxford English Dictionary.

we find no good correlation between the two as the preposition with the most senses is of (16), and that with the fewest is from (1), thus negating the idea that fewer senses make a preposition easier to learn.


This suggests that the frequency effect is not so strong as to over-  Table 9: Confusion matrix for L1 determiners ride any true linguistic information the model has acquired, otherwise the predominant choice would always be the null case.

these results show that the model is indeed capable of distinguishing between contexts which require a determiner and those which do not, but requires further fine tuning to perform better in knowing which of the two determiner options to choose.


For example, a common source of confusion in learners is between by and from, as in I like it because it's from my favourite band.

this confusion is not very frequent in the model, a difference which could be explained either by the fact that, as noted above, performance on from is very low and so the classifier is unlikely to suggest it, or that in training the contexts seen for by are sufficiently distinctive that the classifier is not misled like the learners.


A more detailed discussion of the issues arising from the comparison of confusion pairs cannot be had here.

in noting both divergences and similarities between the two learners, human and machine, we may be able to derive useful insights into the way the learning processes operate, and what factors could be more or less important for them.


For this, we could take all framed nuclei from a corpus and compare the level of ambiguity for differing abstractions.

most framed nuclei occur only once, and it is not clear how meaningful it is to say that these are unambiguous.


Carpenter and Morrill (2005) provided a graph representation and a dynamic programming algorithm for parsing in the Lambek calculus with product.

due to there use of the Lambek calculus with product and to their choice of correctness conditions, they did not obtain a polynomial time algorithm for any significant fragment of the calculus.


The algorithm as described above is a method for answering the decision problem for sequent derivability in the Lambek calculus.

we can annotate the ATGs with the ATGs they are derived from so that a complete set of Roorda-style proof nets, and thus the proofs themselves, can be recovered.


For example, the technology for identifying paraphrases would play an important role in aggregating the wealth of uninhibited opinions about products and services that are available on the Web, from both the consumers and producers viewpoint.

whenever we draw up a document, we always seek the most appropriate expression for conveying our ideas.


0 ≤ α ≤ 1 is a parameter for approximating KL divergence D. The score can be recast into a similarity score via, for example, the following function (Fujita and Sato, 2008): This measure offers an advantage: the weight for each feature is determined theoretically.

the optimization of α is difficult because it varies according to the task and even the data size (confidence of probability distributions).


This result is quite natural because MDS cannot verify the collocation between content words in those cases where a number of function words appear between them.

cFDS with N = 3 could verify this as a result of treating the sequence of function words as a single node.


Moreover, as indicated by the double-headed arrows in the figure, there are a number of bilingual collocations.

as shown in Figure 1, the Reuters document is classified into "Science and Technology," while the Mainichi document is classified into "Space Navigation".


As a result, "U.S. Treasury has no comment on Hashimoto fx remarks" in Reuters category "Forex markets" and the document "Hashimoto" are not retrieved by a single hierarchy approach.

in the integrating method, these two documents are classified correctly into a pair of similar categories, i.e., the "U.S Treasury" is classified into Reuters category "Forex markets", and the "Hashimoto" is classified into Mainichi category "Money and banking".


As shown in Table 6, the performance by integrating hierarchies was much better than that of the non-hierarchical approach, and slightly better than those obtained by a single hierarchy.

correct retrieved collocations were different from each other.


It is interesting to note that 12 of 154 collocations, such as "earn medal" and "block shot" obtained by integrating hierarchies were also obtained by a single hierarchy approach.

other collocations such as "get strikeout" and "make birdie" which were obtained in a particular category (Sport, Baseball) and (Sport, Golf), did not appear in either of the results using a single hierarchy or a non hierarchical approach.


Thus, positive and negative opinions are known as they are separated by reviewers.

they cannot be used directly because Pros and Cons seldom contain comparative words.


In the case of Type 2 comparatives, the situation is similar.

the comparative word ("more", "most", "less" or "least"), the adjective/adverb and the feature are all important in determining the opinion or the preference.


Since we use Pros and Cons as the external information source to help determine whether the combination of a comparative and an entity feature is positive or negative, we need to find comparative and entity features words in Pros and Cons.

in Pros and Cons, comparatives are seldom used (entity features are always there).


The result decides the preferred entity.

point-wise mutual information (pMI) is commonly used for computing the association of two terms (e.g., Turney 2002), which is defined as: we argue that pMI is not a suitable measure for our purpose.


We are more interested in the conditional probability of C (including its synonyms) given F, which is essentially the confidence measure in traditional data mining.

confidence does not handle well the situation where C occurs frequently but F appears rarely.


In XMG, node names are by default local to a class.

explicit IMPORT and EXPORT declarations can be used to make names "visible" to children classes.


Due to our small corpus, we had to limit the  Table 5: Performance of other models number and the complexity of the features we use, since the more features, the more sparse the data, and the more training data needed.

we aim to expand the feature set with more finegrained features.


Current state-of-the-art IE systems are mostly based on general purpose supervised machine learning techniques (e.g., kernel methods).

supervised systems achieve acceptable accuracy only if they are supplied with a sufficiently large amount of training data, usually consisting of manually annotated texts.


Several errors are due to lack of information in WordNet, For example, Leonhard Euler was a mathematician and physicist, however, in WordNet, he is classified as physicist, and our system classifies him as mathematician.

for simplicity, the algorithm returns a single category per instance, however, the test set contains many entities that are classified in more than one category.


The high WordNet::Domains results (DomEntropy and DomTop3%) probably reflect the fact that they are produced using the same resource as the creation of the test sets.

knowledge of correct senses is not required for the homogeneity measures and these scores indicate that they are capable nonetheless of capturing topic homogeneity.


The data under (1) and (2) shortly repeat this well-known pattern.

(1) be quiet for an hour / *in an hour swim for an hour / *in an hour (2) arrive *for an hour / in an hour build a tower *for an hour / in an hour after closer inspection, these claimed test cases do not seem to be as simple and clear cut as one would like, given their fundamental theoretical status.


The semantic material just combines normally via superposition, as can be seen in (11), and the algorithm finishes here.

(11) Λ(V tel in an hour) = a, ¬o, time(m) ¬a, ¬o + ¬a, o, time(n), hour(m,n) in case of an aspectual clash between preposition and event description, combining the concepts leads to a contradiction at some predetermined position inside the complex situational type, as happens in (12).